An Artificial Neural Network (ANN) is a device in which several interconnected elements process information simultaneously, adapting and learning from past patterns. The ANN may be used to predict future responses of a control system that varies with time. For example, electric power consumption in the power generation industry may vary with time. In order to operate the power generation equipment efficiently it may be useful to identify future consumption patterns. Very Short Term Load Predictor (VSTLP) provides a tool for estimating demand for the system power output over a predetermined future time period, for example, 60 minutes or less from the current point in time.
The neural-network-based very short term load predictor (VSTLP) requires that the actual load data sources be determined and made available for offline training of neural networks that are to be used for online prediction and further online training/tuning to improve prediction accuracy performance. The load data of up to five full-day-long load curves is used for offline neural network training. A full-year worth of load data at a specified time interval is stored in the VSTLP database with 10-year worth of load data for holidays stored as well. Accordingly, a need exists for a device, method, and system that make efficient use of the off-line data to provide training of ANN-based VSTLP.